Is it possible to get a graph like the following with ggplot2 in R?
ggplot(mydata, aes(x=discrete_1, y=continuous_1)) +
geom_violin(aes(fill=discrete_2, alpha=continuous_2)) +
facet_grid(. ~ discrete_3)
Trying this, it seems their either 'alpha' or 'fill' will override each other?
I'm open to a different solution that achieves the same result (adding alpha to the fill colours proportionally to the value of continuous_2). My values for continuous_2 are always the same across rows with matching values for discrete_2.
Sorry if this is duplicate, I couldn't find anything that addressed this specifically.
Related
I have a dataset myData which contains x and y values for various Samples. I can create a line plot for a dataset which contains a few Samples with the following pseudocode, and it is a good way to represent this data:
myData <- data.frame(x = 290:450, X52241 = c(..., ..., ...), X75123 = c(..., ..., ...))
myData <- myData %>% gather(Sample, y, -x)
ggplot(myData, aes(x, y)) + geom_line(aes(color=Sample))
Which generates:
This turns into a Spaghetti Plot when I have a lot more Samples added, which makes the information hard to understand, so I want to represent the "hills" of each sample in another way. Preferably, I would like to represent the data as a series of stacked bars, one for each myData$Sample, with transparency inversely related to what is in myData$y. I've tried to represent that data in photoshop (badly) here:
Is there a way to do this? Creating faceted plots using facet_wrap() or facet_grid() doesn't give me what I want (far too many Samples). I would also be open to stacked ridgeline plots using ggridges, but I am not understanding how I would be able to convert absolute values to a stat(density) value needed to plot those.
Any suggestions?
Thanks to u/Joris for the helpful suggestion! Since, I did not find this question elsewhere, I'll go ahead and post the pretty simple solution to my question here for others to find.
Basically, I needed to apply the alpha aesthetic via aes(alpha=y, ...). In theory, I could apply this over any geom. I tried geom_col(), which worked, but the best solution was to use geom_segment(), since all my "bars" were going to be the same length. Also note that I had to "slice" up the segments in order to avoid the problem of overplotting similar to those found here, here, and here.
ggplot(myData, aes(x, Sample)) +
geom_segment(aes(x=x, xend=x-1, y=Sample, yend=Sample, alpha=y), color='blue3', size=14)
That gives us the nice gradient:
Since the max y values are not the same for both lines, if I wanted to "match" the intensity I normalized the data (myDataNorm) and could make the same plot. In my particular case, I kind of preferred bars that did not have a gradient, but which showed a hard edge for the maximum values of y. Here was one solution:
ggplot(myDataNorm, aes(x, Sample)) +
geom_segment(aes(x=x, xend=x-1, y=Sample, y=end=Sample, alpha=ifelse(y>0.9,1,0)) +
theme(legend.position='none')
Better, but I did not like the faint-colored areas that were left. The final code is what gave me something that perfectly captured what I was looking for. I simply moved the ifelse() statement to apply to the x aesthetic, so the parts of the segment drawn were only those with high enough y values. Note my data "starts" at x=290 here. Probably more elegant ways to combine those x and xend terms, but whatever:
ggplot(myDataNorm, aes(x, Sample)) +
geom_segment(aes(
x=ifelse(y>0.9,x,290), xend=ifelse(y>0.9,x-1,290),
y=Sample, yend=Sample), color='blue3', size=14) +
xlim(290,400) # needed to show entire scale
I honestly don't know why this is being so hard.
I'm creating a simple scatter plot. The x axis is a continuous variable, and at every tick in x I need to plot four points with error bars. I'm using position dodge and everything works fine.
Each point has a different color, size and shape as governed by three further variables: color and shape are governed by factors, size by a continuous variable.
By default, the four points reflect the order of the levels in the color variable (red always left, then green, then blue) but I would like them to reflect the order of the size variable (the continuous one), smallest left and largest right. How do I specify that size should be prioritised when ordering points in position dodge? I tried using reverse ordering but then the points are ordered first according to the shape legend.
I could change the mapping between variable and aesthetics (all variables are fundamentally continuous and could be used with size) but I think it'd be useful to know how to specify the order in which multiple variables should be considered when dodging points.
The question is somewhat unclear unfortunately. You don't show "a simple scatter plot". You are showing some statistics (mean with error band??) for specific x values - although this is seemingly continuous, this looks as if you have categorised it beforehand - resulting in some summary statistics which you are plotting.
Also, it is not easy (impossible) to fully help you without knowing what you have done until now to come to where you are.
I have tried to reproduce a similar looking plot with mtcars.
Dodging is only possible by one group (but one group can contain more than one variable). To specify how to group, add group = ... to your aesthetics.
Like so:
library(tidyverse)
ggplot(filter(mtcars, carb %in% 1:4)) +
geom_point(aes(carb, mpg, size= gear, group = gear, shape = as.character(vs), color = as.factor(cyl)),
position = position_dodge(width = .5))
This is now dodged by gear, which is also used as size aesthetic.
How can I plot the relative proportions of two groups using a fill aesthetic in ggplot2?
I am asking this question here because several other answers on this topic seem incorrect (ex1, ex2, and ex3), but Cross Validated seems to have functionally banned R specific questions (CV meta). ..density.. is conceptually related to, but distinct from proportions (ex4 and ex5). So the correct answer does not seem to involve density.
Example:
set.seed(1200)
test <- data.frame(
test1 = factor(sample(letters[1:2], 100, replace = TRUE,prob=c(.25,.75)),ordered=TRUE,levels=letters[1:2]),
test2 = factor(sample(letters[3:8], 100, replace = TRUE),ordered=TRUE,levels=letters[3:8])
)
ggplot(test, aes(test2)) + geom_bar(aes(y = ..density.., group=test1, fill=test1) ,position="dodge")
#For example, the plotted data shows level a x c as being slightly in excess of .15, but a manual calculation shows a value of .138
counts <- with(test,table(test1,test2))
counts/matrix(rowSums(counts),nrow=2,ncol=6)
The answer that seems to yield an output that is correct resorts to a solution that doesn't use ggplot2 (calculating it outside of ggplot2) or requires that a panel be used rather than a fill aesthetic.
Edit: Digging into stat_bin yields that the function ultimately called is bin, but bin only gets passed the values in the x aes. Without rewriting stat_bin (or making another stat_) the hack that was applied in the above referenced answer can be generalized to the fill aes in the absence of the group aes with the following code for the y aes: y = ..count../sapply(fill, FUN=function(x) sum(count[fill == x])). This just replaces PANEL (the hidden column that is present at the end of StatBin) with fill). Presumably other hidden variables could get the same treatment.
This is an aweful hack, but it seems to do what you want...
ggplot(test, aes(test2)) + geom_bar(aes(y = ..count../rep(c(sum(..count..[1:6]), sum(..count..[7:12])), each=6),
group=test1, fill=test1) ,position="dodge") +
scale_y_continuous(name="proportion")
I am making a graph in ggplot2 consisting of a set of datapoints plotted as points, with the lines predicted by a fitted model overlaid. The general idea of the graph looks something like this:
names <- c(1,1,1,2,2,2,3,3,3)
xvals <- c(1:9)
yvals <- c(1,2,3,10,11,12,15,16,17)
pvals <- c(1.1,2.1,3.1,11,12,13,14,15,16)
ex_data <- data.frame(names,xvals,yvals,pvals)
ex_data$names <- factor(ex_data$names)
graph <- ggplot(data=ex_data, aes(x=xvals, y=yvals, color=names))
print(graph + geom_point() + geom_line(aes(x=xvals, y=pvals)))
As you can see, both the lines and the points are colored by a categorical variable ('names' in this case). I would like the legend to contain 2 entries: a dot labeled 'Data', and a line labeled 'Fitted' (to denote that the dots are real data and the lines are fits). However, I cannot seem to get this to work. The (awesome) guide here is great for formatting, but doesn't deal with the actual entries, while I have tried the technique here to no avail, i.e.
print(graph + scale_colour_manual("", values=c("green", "blue", "red"))
+ scale_shape_manual("", values=c(19,NA,NA))
+ scale_linetype_manual("",values=c(0,1,1)))
The main trouble is that, in my actual data, there are >200 different categories for 'names,' while I only want the 2 entries I mentioned above in the legend. Doing this with my actual data just produces a meaningless legend that runs off the page, because the legend is trying to be a key for the colors (of which I have way too many).
I'd appreciate any help!
I think this is close to what you want:
ggplot(ex_data, aes(x=xvals, group=names)) +
geom_point(aes(y=yvals, shape='data', linetype='data')) +
geom_line(aes(y=pvals, shape='fitted', linetype='fitted')) +
scale_shape_manual('', values=c(19, NA)) +
scale_linetype_manual('', values=c(0, 1))
The idea is that you specify two aesthetics (linetype and shape) for both lines and points, even though it makes no sense, say, for a point to have a linetype aesthetic. Then you manually map these "nonsense" aesthetics to "null" values (NA and 0 in this case), using a manual scale.
This has been answered already, but based on feedback I got to another question (How can I fix this strange behavior of legend in ggplot2?) this tweak may be helpful to others and may save you headaches (sorry couldn't put as a comment to the previous answer):
ggplot(ex_data, aes(x=xvals, group=names)) +
geom_point(aes(y=yvals, shape='data', linetype='data')) +
geom_line(aes(y=pvals, shape='fitted', linetype='fitted')) +
scale_shape_manual('', values=c('data'=19, 'fitted'=NA)) +
scale_linetype_manual('', values=c('data'=0, 'fitted'=1))
I have some data for which, at one level of a factor, there is a significant correlation. At the other level, there is none. Plotting these side-by-side is simple. Adding a line to both of them with stat_smooth, also straightforward. However, I do not want the line or its fill displayed in one of the two facets. Is there a simple way to do this? Perhaps specifying a blank color for the fill and colour of one of the lines somehow?
Don't think about picking a facet, think supplying a subset of your data to stat_smooth:
ggplot(df, aes(x, y)) +
geom_point() +
geom_smooth(data = subset(df, z =="a")) +
facet_wrap(~ z)
Of course, I later answered my own question. Although, is there a less hack-y way to do this? I wonder if one could even fit different functions to different panels.
One technique is to use + scale_fill_manual and scale_colour_manual. They allow one to specify what colors will be used. So, in this case, let's say you have
a<-qplot(x, y, facets=~z)+stat_smooth(method="lm", aes(colour=z, fill=z))
You can specify colors for the fill and colour using the following. Note, the second color is clear, as it is using a hex value with the final two numbers representing transparency. So, 00=clear.
a+stat_fill_manual(values=c("grey", "#11111100"))+scale_colour_manual(values=c("blue", "#11111100"))